Specially, the authors used 5 different emojis to represent the 5 degrees of humor instead of using the 5-point annotation.

while previous work focuses on textual humor annotation of humorous/non-humorous and degree of funniness, such annotations do not provide adequate knowledge and scenarios to explain how humor arises, so they may not provide a deep analysis of the underlying mechanism of humor.


Existing automatic anagram generation methods can find possible combinations of words form an anagram.

they do not pay much attention to the naturalness of the generated anagrams.


NLP tasks (Józefowicz et al., 2016;Gehrmann et al., 2018;Skadina and Pinnis, 2017).

beam search, the de-facto decoding algorithm used in many language generation methods, cannot be used as-is to generate anagrams since it cannot ensure that the generated sentences are anagrams.


Dancing links is a link-based data structure that supports efficient DFS for some combinatorial search problems (Details are shown in (Knuth, 2019)).

we found that using a simple array-based representation is sufficient since the conditional probability evaluation consumes most of the time.


While the baseline method seems to employ some heuristics for deciding the word order, the combinations of selected words tend to form meaningless sentences.

the proposed method tends to generate readable sentences.


This is due to the fact that the famous anagrams use relatively rare words.

the generated anagrams contain natural sentences.


Here, the word 'elsewhere' is the key cue which determines the stance.

the presence of the word 'elsewhere' does not necessarily imply that the perspective is opposing the claim.


(2016) proposed the Deep-Regex model based on Seq2Seq for generating regular expressions from natural language descriptions together with a dataset of 10,000 NL-RX pairs.

due to the limitations of the standard Seq2Seq model, the Deep-Regex model can only generate regular expressions similar in shape to the training data.


The KB13 (Kushman and Barzilay, 2013) dataset was constructed by regex experts and is relatively small.

nL-RX-Synth is data generated automatically and nL-RX-Turk is made from ordinary people by paraphrasing nL descriptions in nL-RX-Synth using Mechanical Turk (Locascio et al., 2016).


For in-hospital mortality task, only those patients are considered who were admitted in the ICU for at least 48 hours.

we dropped all clinical notes which doesn't have any chart time associated and also dropped all the patients without any notes.


CGAEW improves upon CGAE as the existence of world-state in the score of the attention layer allows the model better learn the grounding of entities in the instruction to the map.

our best model still fails on features not captured by our world-state: abstract unmarked entities such as blocks, intersections, etc, and generic entities such as traffic-lights (Tab.


That is to say, if a 2-tuple often cooccurs with CC i in training corpus with positive view, it contributes more to positive orientation than negative one.

if the 2-tuple often co-occurs with CC i in training corpus with negative view, it contributes more to negative orientation.


Sentiment classifying knowledge is defined as the importance of all 2-tuples <word, epos> that compose the context of CC i (given as an example) to sentiment classification and every Concerned Concept like CC i has its own positive and negative sentiment classifying knowledge that can be formalized as a 3-tuple K: To CC i , its S i pos has concrete form that is described as a set of 5-tuples: Where S i pos represents the positive sentiment classifying knowledge of CC i , and it is a data set about all 2-tuples <word, epos> appearing in the sentences containing CC i in training texts with positive view.

s i neg is acquired from the training texts with negative view.


Seen from the table, when evaluating texts that have more than 15 sentences, for enough features, SVM has better result, while ours is averagely close to it.

when evaluating the texts containing less than 15 sentences, our method is obviously superior to SVM in either positive or negative view.


We can see from both Tables 3 and 4 that when the number of words in a sequence is small, the result has no effect with the number of positive training data, since the range of F-score in Table 3 is 0.415 ∼ 0.478, and that in Table 4  is 0.453 ∼ 0.516.

as we can see from Table 2, when the number of title words is large, the smaller the number of positive training data is, the worse the result is.


Overall, the result of 'with hierarchy' was better than that of 'without hierarchy' in all N t values.

there are four topics/N t patterns whose results with hierarchical classification were worse than those of without a hierarchy.


Last decade has witnessed an explosive growth of multimedia information such as images and videos.

we can't access to or make use of the relevant information more leisurely unless it is organized so as to provide efficient browsing and querying.


In our annotation procedure, each annotated word is predicted independently by the Maximum Entropy Model, word correlations are not taken into consideration.

correlations between annotated words are essentially important in predicting relevant text descriptions.


Japanese relative clause constructions should be classified into at least two major semantic types: case-slot gapping and head restrictive.

these types for relative clause constructions cannot apparently be distinguished.


Cooccurrence information between nouns and verbs can be calculated from the syntactically parsed corpus, and this information can be used preferentially instead of handcrafted case frames to determine whether a noun can be the filler of a case-slot of a verb [7,11].

merely using the cooccurrence information between nouns and verbs instead of case frames cannot provide a good solution to the analysis of Japanese relative clauses.


For nouns that do not tend to be modified by 'outer' clauses, such as " "(people), " " (city), and " "(television), the ratio between the frequency and the number of verbs is almost the same between the relative clause and case-slot cases.

for nouns that tend to be modified by 'outer' clauses, such as " "(intent), " " (fact), and " "(preparation), the number of verbs is much bigger in relation to clause cases, although the frequency is smaller.


As previously explained, if the head noun can fill the case-slot of the main verb of the relative clause, the RCC instance can be judged as an 'inner' clause.

if the case-slot that the head noun can fill is already filled by the noun in the relative clause, and hence unavailable for case-slot gapping, the rule cannot be applied.


The second interpretation is that " " can be the direct object(" " case) of the main verb " " and can be considered to be modified by the 'inner' relative clause.

(b) has only the interpretation of 'inner'.


If modifiers other than the relative clause exist, RCC type is 'inner'.

the accuracy of this feature is not so good compared with other features.


We found use of lexical collocations did yield a small (0.3%) but statistically significant improvement in performance over the unmodified parser (Table 5).

when combined with either POS or entity adaptations, the lexicon's impact on parsing accuracy was statistically insignificant.


Domain knowledge appears to be necessary here for correct resolution.

to this, POS tags appear to be a distributional rather than a semantic concern.


For a better recognition, one can define accurate regular expressions.

we just collect suffixes and feature characters to match strings.


For example, the verb eat impose a selection restriction on its object modifier 5 : it has to be solid food.

the verb drink specifies its object modifier to be liquid beverage.


In this case, the condition B max will not suffice, and we need a second boundary condition: B increase Boundaries are locations where the entropy is increased.

when h decreases at k = 5, then even B increase cannot be applied to detect k = 5 as a boundary.


8 The problem here might not occur if we used many more question types in the first QA test.

we did not do this to keep the first QA test simple.


[16] proposed a method of identifying attribute-value pairs in Web documents.

since this method only identified the attributes obtained with the method in [1], the coverage might be bounded by the coverage of tables for attributes.


However, these QA systems assumed the user model where the user asks what type questions.

there are a few QA systems which assumed the user model where the user asks how type question, in other words, how to do something and how to cope with some problem [3] [4] [7].


To solve this problem, [3] and [4] proposed methods of collecting knowledge from FAQ documents and technical manuals by using the document structure, such as, a dictionary-like structure and if-then format description.

mails posted to a mailing list, such as Vine Users ML, do not have a firm structure because questions and their answers are described in various ways.


Because of no firm structure, it is difficult to extract precise information from mails posted to a mailing list in the same way as [3] and [4] did.

a mail posted to ML generally has a significant sentence.


As a result, the set of (Q4) and (DA4-1) was determined as correct.

the set of (Q4) and (DA4-2) was a failure.


This is because there is not much likelihood of matching user's question and wrong significant sentence extracted from question mails.

failures which were caused by wrong significant sentences extracted from DA mails were serious.


In Test 1, our system answered question 2, 6, 7, 8, 13, 14, 15, 19, and 24.

the full text retrieval system answered question 2, 5, 7, 19, and 32.


proposed two iterative scaling algorithms to find parameters for CRFs.

these methods converge into a global maximum very slowly.


This is why a rule-based chunking method is predominantly used.

with sophisticated rules, the rule-based chunking method has limitations when handling exceptional cases.


Note that we only invoke instantiate-hypothesis() on complete, top-level hypotheses, as the ME features of Toutanova and Manning [20] can actually be evaluated prior to building each full feature structure.

the procedure could be adapted to perform instantiation of sub-hypotheses within each local search, should additional features require it.


Treebanks provide instances of phrasal structures and their statistical distributions.

none of treebanks provide sufficient amount of samples which cover all types of phrasal structures, in particular, for the languages without inflectional markers, such as Chinese.


It results that grammars directly extracted from treebanks suffer low coverage rate and low precision [7].

arbitrarily generalizing applicable rule patterns may cause over-generation and increase ambiguities.


NP S rules and NP VP rules will be derived each respectively from subject NP and object NP structures.

such difference seems not very significant in Chinese.


The maximization of the likelihood with the above model is equivalent to finding the model p M that is closest to the reference probability p 0 in terms of the Kullback-Leibler distance.

we cannot simply apply the above method to our task because the parameter estimation requires the computation of the above probability for all parse candidates T (s).


The main cost of using a large feature set is the increase of training time.

this may be paid off by giving the learner a better chance to achieve a better model.


It is particularly suitable for our research purpose.

to BNC and Brown corpus, the WSJ corpus indeed contains many more dots used in different ways for various purposes.


Their method has a remarkable advantage in that synonyms do not need to be surrounded with the same words.

their method is not applicable to structurally different MCTs.


From S1 and S2, the word pair "Monetary Union" and "Finance Minister Engoran" can be extracted.

the word "Monetary" in S1 does appear in the synonym part of S2 but does appear in another part of S2.


This result suggests that our method could capture proper places of MCT pairs with this level of precision.

this precision falls to 70.0% without source texts that represents synonym acquisition precision.


Following the condition of MTCC data, the outside-appearance checking range covers entire texts, i.e., outside appearance should be checked throughout an article.

this condition is too expensive to follow since text length is much longer than that of MTCC data.


Experience showed that the lexicon learned in the candidate generation stage, while adequate for candidate generation, is not of sufficient quality for biparsing due to the non-parallel nature of the training data.

any translation lexicon of reasonable accuracy can be used.


No direct comparison of this figure is possible since previous work has focused on the rather different objectives of mining noisy parallel or comparable corpora to extract comparable sentence pairs and loose translations.

we can understand the improvement by comparing against scores obtained using the cosine-based lexical similarity metric which is typical of the majority of previous methods for mining non-parallel corpora, including that of Fung and Cheung (2004) [9].


Many methods of term extraction have been discussed in terms of their accuracy on huge corpora.

when we try to apply various methods that derive from frequency to a small corpus, we may not be able to achieve sufficient accuracy because of the shortage of statistical information on frequency.


It is obvious that this sort of information should be carefully controlled.

the filtering performance using the existing methodologies is still not satisfactory in general.


Using words as features in the first step aims at its better statistical coverage, --the 500 selected features in the first step can treat a majority of documents, constituting 63.13% of the test set.

using word bigrams as features in the second step aims at its better discriminating capability, although the number of features becomes comparatively large (3000).


Structure-mapping assumes that the causal behaviour of a concept is expressed in an explicit, graph-theoretic form so that unifying sub-graph isomorphisms can be found between different representations.

abstraction theories assume that analogous concepts, even when far removed in ontological terms, will nonetheless share a common hypernym that captures their causal similarity.


Thus, we should expect an analogous pairing like surgeon and butcher to have different immediate hypernyms but to ultimately share an abstraction like cutting-agent (see [8,9]).

the idea that a standard ontology will actually provide a hypernym like cutting-agent seems convenient almost to the point of incredulity.


PWN is differential is nature: rather than attempting to express the meaning of a word explicitly, PWN instead differentiates words with different meanings by placing them in different synsets, and further differentiates synsets from one another by assigning them to different positions in its ontology.

howNet is constructive in nature, exploiting sememes from a less discriminating taxonomy than PWN's to compose a semantic representation of meaning for each word sense.


Since this new taxonomy is derived from the use of {~} in HowNet definitions, both the coverage and recall of analogy generation crucially depend on the widespread use of this reflexive construct.

of the 23,505 unique definitions in HowNet, just 6430 employ thus form of self-reference.


These results, along with several studies, also show the superiority of Skew Divergence.

measures for vectors such as Euclidean distance achieved relatively poor performance compared to those for probability distributions.


The problems mentioned earlier make it more difficult to match concepts by the algorithm.

we can use the algorithm to identify where the problems occur.


There have been several works to build taxonomy of nouns from an MRD.

most of them relied on the lexico-syntactic patterns compiled by human experts.


A relation is basically an ordered pair, thus "Sam was flown to Canada" contains the relation AT(Sam, Canada) but not AT(Canada, Sam).

7 relations in NEAR (relative location) and SOCIAL (associate, other-personal, other-professional, other-relative, sibling, and spouse) types are symmetric.


This does not necessarily mean that we can choose NEAR when the entity distance in an example is close to 3.23.

there is an interesting result about NEAR when we apply the classifier trained on training data to held-out data.


2) Similarity calculation: The similarity between two relation instances is defined between two parse trees.

the state-of-the-art of parser is always error-prone.


This also prevents them from working well on less-frequent data [8].

for the similarity function in our approach, the best threshold is much greater than 0.


Assuming we have -Input (instance) space X and output (label) space Y -Labeled data set L and unlabeled data set U (as mentioned before, no distinction is made between "unlabeled" and "test" data in the transductive learning setting) One could distinguish three types of learning paradigms: where f represents the induced model -Induction with unlabeled data The three learning paradigms clearly have different advantages and different application scenarios.

when it comes to exploiting unlabeled data, the tradeoff between the last two is not yet well understood.


it may be worth the effort to investigate other alternatives.


"12/02/1809"), it is not the case for question [Eb], because a proper answer could be "35,000 years ago".

if it is known that the time granularity concerned is "thousands of years", answer extraction turn to be more targeted.


the questions beginning with "which year" or "for how many years".


In these workshops, the inputs to systems are only single-sentence questions, which are defined as the questions composed of one sentence.

on the web there are a lot of multiple-sentence questions (e.g., answer bank 3 , AskAnOwner 4 ), which are defined as the questions composed of two or more sentences: For example, "My computer reboots as soon as it gets started.


[2] used hand-crafted rules for question classification.

methods based on pattern matching have the following two drawbacks: high cost of making rules or patterns by hand and low coverage.


Therefore, we also use SVM in classifying questions, as we will explain later.

please note that we treat not only usual single-sentence questions, but also multiple-sentence questions.


In many researches, question focuses are extracted with hand-crafted rules.

since we treat all kinds of questions including the questions which are not in an interrogative form, such as "Please teach me -" and "I don't know -", it is difficult to manually create a comprehensive set of rules.


In terms of distribution, the vowel /\/ does not occur at the beginning of a syllable except in the conjugational variants of verbs formed from the verbal stem /k\r\/ (to do.).

to this, though the letter " " exists in Sinhala writing system (corresponding to the consonant sound /j/), it is not considered a phoneme in Sinhala.


They simplify a machine transliteration problem into either ψ G or ψ P assuming that one of ψ G and ψ P is able to cover all transliteration behaviors.

transliteration is a complex process, which does not rely on either source grapheme or phoneme.


ψ H does not consider correspondence between source grapheme and phoneme during the transliteration process.

the correspondence plays important roles in machine transliteration.


A language model is also composed of the words in the training corpus.

the use of a full-form word itself may cause severe data sparseness problem, especially relevant for more inflectional/agglutinative languages like Japanese and Korean.


Korean-to-Chinese translation), the performance was the worst compared with other language pairs and directions in BLEU and mWER.

the performance of Chinese-to-Korean was much better than Korean-to-Chinese, meaning that it is easier to generate Korean sentence from Chinese the same as in Japanese-to-Korean and Englishto-Korean.


In addition, many other features, such as the answer candidate frequency, can be extracted based on the IR output and are thought as the indicative evidences for the answer extraction [10].

in this paper, we are to evaluate the answer extraction module independently, so we do not incorporate such features in the current model.


A naive solution to query formulation is using the keywords in an input question as the query to a search engine.

it is possible that the keywords may not appear in those answer passages which contain answers to the given question.


With the second constraint, we can delete '明明天' because '明明' and '明天' are words in the dictionary.

'小 明 ' , '小明明', and '小明明天' will be kept because '小' is a "unattached" single character.


The data sparseness problem is practically non-existent in the character-based model for the Chinese character set is limited.

odd characters are occasionally found in Chinese personal or place names.


Galiano, Valdivia, Santiago and Lopez [14] use five statistical measures to classify generic MWEs using the LVQ (Learning Vector Quantization) algorithm.

we do a more detailed and focussed study of V-N collocations and the ability of various classifiers in recognizing MWEs.


Because we focused on bigrams in this paper, MWEs of longer than two tokens were ignored when assessing whether a candidate MWE was a true or false MWE.

some of these candidate MWEs were in fact substrings of a longer MWE.


Early approach to statistical machine translation relies on the word-based translation model to describe the translation process [1].

the underlying assumption of word-to-word translation often fails to capture all properties of the language, i.e.


In the case of English to French translation, we follow the phrases in the English order.

it can be done along the target language as well since our approach follows a symmetric many-to-many word alignment strategy.


The most current guidelines of ST specify that zeros are dropped in order to maintain the consistency and efficiency of the treebank.

pKT advocates for representing zero elements.


We extracted only 100 sentences from the ST corpus containing natural spoken conversations and found that 81 sentences are represented as VPs or VNPs (predicate nominal phrases).

it may derive a misleading generalization such that canonical sentence patterns in the given corpus are VPs or VNPs.


In line with this, semantic interpretations of those incomplete VPs or VNPs subsume the meaning of the zero pronouns whose antecedents appear in the previous utterances.

zero-less mark-up poses a difficulty in retrieving the complete sentential meaning from the given phrasal categories of VPs or VNPs.


For example, one of the most frequently discussed topics in Korean grammar is formation of Double Subject Constructions (DSCs), which license two subjects.

zero-less treebanks do not correctly represent Double Subject Constructions and represent (5) and (6) differently in spite of their similarity in argument realization.


The first is classified as a grammatical topic marker while the latter is a contrastive topic marker in traditional Korean grammar.

the current annotations of PKT and ST treat topic marker nun as the same auxiliary postposition, which is similar to other postpositions man 'only', to 'also', and mace 'even'.


The reason why Koehn method outperforms IBM method D may be due to the different decoding strategy.

we still need further investigation to understand why Koehn method outperforms IBM method D significantly.


In our study, we enhance the LMM with the PM to account for the word reordering issue in NE translation, so our model is capable of modeling the non-monotone problem.


Bangalore and Riccardi [21] proposed a phrase-based variable length n-gram model followed by a reordering scheme for spoken language translation.

their re-ordering scheme was not evaluated by empirical experiments.


Chronological ordering; ordering sentences according to the published date of the documents they belong to [6], is one solution to this problem.

showing that this approach is insufficient, Barzilay [1] proposed an refined algorithm which integrates chronology ordering with topical relatedness of documents.


For sentences taken from the same document we keep the order in that document as done in single document summarization.

we have to be careful when ordering sentences which belong to different documents.


Both Kendall's τ coefficient and the Weighted Kendall coefficient measure discordants between ranks.

in the case of summaries, we need a metric which expresses the continuity of the sentences.


TSC-3 corpus contains human selected extracts for 30 different topics.

in the TSC corpus the extracted sentences are not ordered to make a readable summary.


However, we cannot directly compare Lapata's [3] approach with our probabilistic expert as we do not use dependency ANOVA test of the results shows a statistically significant difference among the five methods compared in table 2 under 0.05 confidence level.

we could not find a statistically significant difference between CO and LO.


where U T U = I, V T V = I, and W is the diagonal matrix of singualr values.

to our usage of SVD, [3] used term-document matrix: our sentence-term matrix can be regarded as the transpose of term-document matrix, since the documents can be thought of the sentences in the summarization fields.


In [7], the importance of each sentence is computed by repeatedly summing 1 for each occurrence of significant terms in the sentence.

the proposed method can be regarded as more formal or reasonable, since the Euclidean distance between vectors is used to calculate the degree of importance of each sentence.


The label at position i, s i is one of B, I and O.

to the ME model, since B is the beginning of a term, the transition from O to I is not possible.


if a word frequently occurs in other positions, we regard it has the property of a modifying noun.


That is, terms belonging to other classes in GENIA are excluded from the recognition target.

we consider all NEs in the boundary detection step since we separate the NER task into two phases.


[12] calculated accuracy, confidence and score of their patterns to select better patterns.

those statistical measures are calculated only using data obtained from their training corpus which cannot often give enough information.


To resolve these inconsistencies, we basically use statistical measures such as the score of a boundary pattern, Score(p), and the rank of an entity candidate, Rank(e), as in most previous works.

this strategy is not always the best because some trivial errors can also be removed by simple heuristics and linguistic knowledge.


A new bilingual dictionary can be built using two existing bilingual dictionaries, such as Japanese-English and English-Chinese to build Japanese-Chinese dictionary.

japanese and Chinese are nearer languages than English, there should be a more direct way of doing this.


Their method can make the similar Chinese words to have higher ranking but cannot generate new translation candidates.


Using English as the pivot language is a good starting point to construct a new language pair.

there remain a lot of words for which the translations cannot be obtained.


It is fair enough if we translate the single character words using the conversion table.

these characters should have more translations of other multi-character words.


In our survey, only 33% of nouns and 44% of verbal nouns created by kanji/hanzi conversion method exist in the Peking University dictionary.

this may be due to the incompleteness of the Chinese dictionary that we used.


Then, it is passed to the next methods, and segmented incorrectly.

the overlook of the dictionary took place in the following cases: the reason of lowering recall, that is, the overlook of compounds, can be summarized as follows: − Especially for shorter words, it is actually very hard to set up clear criteria for compounds.


In the first example "十分/very" is an adverb and has no temporal meaning.

the character "十/ten" and "分/minute" can be looked up and satisfy the grammar rule.


Because each possible substring in a sentence is tried, multiple nested, overlap or adjacent temporal expressions may exist in the sentence.

some of these expressions are just parts of the optimal answers.


Based on the assumption that two adjacent or overlapped temporal expressions refer to the same temporal concept, we combined them.

the procedure of combination can not help to explain the meaning of the expressions.


The recall, up to above 70%, is higher than all the other three systems.

the precision at the same time is unfortunately the lowest.


Therefore we need to extract the key lexicons from UMLS for each semantic type in background processing and use them to tag unknown chunk with predicted types.

the semantic type checking for pronominal anaphors is done through the extraction of the key verbs for each semantic type.


They developed a discourse-annotated corpus and learned a discourse-parsing algorithm from it.

our discourse analyzer was based on generic heuristic rules.


When human writers create definitions, they take care of the structural elements and requirements described above.

when creating conceptual networks, dictionaries and language based examples are often employed as knowledge sources to determine lexical and semantic relations between words.


Normally, this task would be performed by a parser.

since the CoNLL dataset contains no parsing information 6 and we did not want to use any resources not explicitly provided in the CoNLL data, we had to construct a PPA classifier to specifically perform this task.


Preposition SRL combined with [9] (P = precision, R = recall, F = F-score; above-baseline results in boldface) in the testing data, even when oracled outputs from all three subsystems are used (recall = 18.15%).

this is not surprising because we expected the majority of semantic roles to be noun phrases.


The reason for the worse results is that in our experiments, the oracled PPA always identifies more prepositions attached to verbs than the PPA classifier, therefore more prepositions will be given semantic roles by the SRD classifier.

since the performance of the SRD classifier is not high, and the segmentation subsystem does not always produce the same semantic role boundaries as the CoNLL data set, most of these additional prepositions would either be given a wrong semantic role or wrong phrasal extent (or both), thereby causing the overall performance to fall.


Hence, on the task of identifying incorrect relations in VERBOCEAN, our system has a precision of 85.7%, where precision is defined as the percentage of correctly identified erroneous relations.

it only achieved a recall of 16.2%, where recall is the percentage of erroneous relations that our system identified.


On one hand, we associate the words in the left half with food or cooking.

we associate those in the right half with animals.


Named entities are important constituents to identify roles, meanings, and relationships in natural language sentences.

named entities are productive, so that it is difficult to collect them in a lexicon exhaustively.


The most powerful external feature of creation titles is French quotes "《》", which is defined to represent book names in the set of standard Simplified Chinese punctuation marks of China [5].

they are not standard punctuation marks in Traditional Chinese.


Typically, agent and patient assemblies would be fixed in a case-role representation without such a discriminator, and the model required to learn to instantiate them correctly [10].

we found that the model performed much better when the task was recast as having to learn to isolate the nouns in the order in which they are introduced, and separately mark how those nouns relate to the verb.


Such as, whether or not they take objects, what kind of objects they take, which types of case markers they require to subject and object constituents, and so on.

the attributions of verbs themselves are very important, which determine the quantity of arguments and the frame of sentences, such as attribution of human verbs, of volition verbs, of controllable verbs, causative verbs, and so on.


It is noticed that the granularity of sequences does not seem to yield significantly different performance based on the current data.

whether this is true in general remains an open question.


Each rule in our model has been created relying on a set of linguistic tests used in the theory of LCS and our linguistic intuition on handling LCS.

the rule set was not sufficiently sophisticated, so that led to 59 errors.


He evaluated the method on 12 English nouns, and showed over than 90% precision.

the evaluation was conducted on just a small part of senses of the words, not on all senses of them.


However, the evaluation was conducted on a small part of senses of the target words like [8].

many senses in WordNet do not have unambiguous relatives through relationships such as synonyms, direct hypernyms, and direct hyponyms.


Word sense disambiguation (WSD) has been regarded as essential or necessary in many high-level NLP applications that require a certain degree of semantic interpretation, such as machine translation, information retrieval (IR) and question answering, etc.

previous investigations into the role of WSD in IR have shown that low accuracy in WSD negated any possible performance increase from ambiguity resolution [1,2].


Recently, there has been work on the automatic (or semi-automatic) interpretation of NCs [4,7,8].

most of this work is based on a simplifying assumption as to the scope of semantic relations or the domain of interpretation, making it difficult to compare the performance of NC interpretation in a broader context.


It supports our idea of interpretation of NCs by similarity.

as [19] point out, information on relatedness has not been developed as actively as conceptual similarity.


If we were to use the crossover point (similarity ≥ 0.57), we would clearly "infect" the training data with a significant number of misclassified instances, namely 30.69% of the new training instances; this would have an unpredictable impact on classification performance.

if we were to select a higher threshold based on a higher estimated proportion of correctly-classified instances (e.g.


These settings might lead to an unfair comparison, as the perceptron algorithm will select far more features than the boosting and MSR algorithm.

we used these settings as they all converged under these settings.


Our syntactic elements of expression capture differences in the way people express content and could be useful for authorship attribution.

the experiments we present here indicate that syntactic elements of expression are more successful at identifying expression in individual books while function words are more successful at identifying authors.


This definition is valid if we can assume that the word difficulty is independent of such factors as context or listeners.

we think such assumption is not always true.


Lexical choice has been widely discussed in both paraphrasing and natural language generation (NLG).

to the best of our knowledge, no researches address topic adaptation.


Its adaptation-guided retrieval makes it ultimately similar to our system.

our approach differs from it in two respects.


Many previous researches focused on extracting bilingual lexicon from parallel corpora, and readers can refer to the reviews [1], [2] for the details.

due to the restriction of current available parallel corpora of different languages, together with the fact that corpus annotation requires a lot of manpower and resources, researchers have attempted to extract translations from non-parallel corpus or Web data.


Using the translation combination of each constituent to acquire the translation of a multiword term is very suitable for translation acquisitions of base noun phrases.

terminologies and technical terms often consist of unknown words, and their translations are seldom the combination of each constituent.


Since his research focused on finding English translation given a Japanese term, the segmentation of Japanese could be avoided.

our problem is to find Chinese equivalent using English term, so we have to cope with how to obtain the correct boundary of Chinese translations.


All possible forms of terminology translations can be comprehensively mined after character-based string frequency estimation.

there are many irrelevant items and redundancy noises formed in the process of mining.


We use the "vowel insertion rule" which is also used for verb phrases to check whether vowel elimination occurred in the content word and insert the eliminated vowel.

if the phrase is identified as a loanword phrase, we do not use the vowel insertion rule.


We take the logarithm of FSR 3 Although there have been many existing works in this direction (Lua and Gan, 1994;Chien, 1997;Sun et al., 1998;Zhang et al., 2000;SUN et al., 2004), we have to skip the details of comparing MI due to the length limitation of this paper.

our experiments with MI provide no evidence against the conclusions in this paper.


Note that boundary F-measure gives much more higher score than word F-measure for the same segmentation output.

in either of metric, we can find no evidence in favor of decoding algorithm (3).


When we look at recall and precision numbers directly, we observe that even without rules, the algorithm produces large recall boosts (especially after splitting).

these boosts are accompanied by precision losses, which result in unchanged or lower F-scores.


It is well known that syntactic structured information plays a critical role in many critical NLP applications, such as parsing, semantic role labeling, semantic relation extraction and co-reference resolution.

it is still an open question on what kinds of syntactic structured information are effective and how to well incorporate such structured information in these applications.


Among previous tree kernels, the convolution tree kernel represents the state-of-the-art and have been successfully applied by Collins and Duffy (2002) on parsing, Moschitti (2004) on semantic role labeling, Zhang et al (2006) on semantic relation extraction and Yang et al (2006) on pronoun resolution.

there exist two problems in Collins and Duffy's kernel.


This convolution tree kernel has been successfully applied by Yang et al (2006) in pronoun resolution.

there is one problem with this tree kernel: the subtrees involved in the tree kernel computation are context-free (That is, they do not consider the information outside the sub-trees.).


On the other hand, unsupervised learning-based methods do not need the definition of relation types and the availability of manually labeled data.

they fail to classify exact relation types between two entities and their performance is normally very low.


However, extending with synonym is mainly adding repetitious information, which can not define the topics more clearly.

topicbased research should be real-sensitive.


The preprocessing, feature weighting and similarity computation in dynamic method are similar as those in baseline method.

the vector representation for a story here is dynamic.


In the baseline method, term vectors are dynamic because of the incremental tf * idf weighting.

dynamic information extending is another more important reason in the dynamic method.


The difference between two dynamic methods is due to different in the P miss .

it is too little to compare the two dynamic systems.


The experiment results indicate that this method can effectively improve the performance on both miss and false alarm rates, especially the later one.

we should realize that there are still some problems to solve in story link detection.


Therefore, bigram SUM and bigram PP methods easily achieve good performance for English origin.

for Japanese origin (represented by 1413 Chinese characters) and Chinese origin (represented by 2319 Chinese characters), the data sparseness becomes acute and causes performance degradation in SUM and PP models.


In case the foreign word is not found in CMU Speech dictionary, we guess its pronunciation using the method described by Oh and Choi.

in this case, the context window size is 3.


These word groups thus seem to be similar to chunks as generally understood (Molina and Pla, 2002;Megyesi, 2002).

chunks in UCSG are required to correspond to thematic roles, which means for example, that prepositional phrases are handled properly.


Extensive experimentation has been carried out to evaluate the performance of the system.

direct comparisons with other chunkers and parsers are not feasible as the architectures are quite different.


Memory-Inductive Categorial Grammar, abbreviated MICG, is a version of pure categorial grammar extended by ellipsis-based analysis.

it relies on antecedent memorization, gap induction, and gap resolution that outperform CCG's functional composition and type raising.


Unithood concerns with whether or not a sequence of words should be combined to form a more stable lexical unit.

termhood measures the degree to which these stable lexical units are related to domain-specific concepts.


(Seretan et al., 2004) tested mutual information, loglikelihood ratio and t-tests to examine the use of results from web search engines for determining the collocational strength of word pairs.


For example, "E. coli food poisoning", "E. coli" and "food poisoning" are acceptable as valid complex term candidates.


If a candidate word has a 'cohesive relation' with the words in the chain it is added to the chain.

if a candidate word is not related to any of the chains, a new chain is created for the candidate word.


According to the analysis in Section 2, our basic idea is clear that we regard the supervised summarizing as a problem of sequence segmentation.

in our approach, the features are not only obtained on the sentence level but also on the segment level.


8.3% and 4.9% improvements are respectively gained comparing to LSA and HITS models Currently, the main problem of our method is that the searching space goes large by using the extended features and semi-CRF, so the training procedure is time-consuming.

it is not so unbearable, as it has been proved in (Sarawagi and Cohen, 2004).


(1999; proposed a method based on supervised machine learning to identify whether two paragraphs contain similar information.

we found it was difficult to accurately identify EQ pairs between two sentences simply by using similarities as features.


Finally, the best SemEval 2007 Web People Search system (Chen and Martin, 2007) used techniques similar to ours: named entity recognition using off-the-shelf systems.

in addition to semantic information and full document condition they also explore the use of contextual information such as the url where the document comes from.


We have also shown that one system which uses one specific type of semantic information achieves stateof-the-art performance.

more work is needed, in order to understand variation in performance from one data set to another.


Recently, transfer learning problem was tackled by applying EM algorithm along with the naive Bayes classifier (Dai et al., 2007).

they all are monolingual text categorization tasks.


In addition, each source word can be translated by several related target words and the latter being weighted.

among the proposed translation words, there may be irrelevant ones.


On one hand, the confidence measure allows us to adjust the original weights of the translations and to select the best translation terms according to all the information.

the confidence measures also provide us with a new weighting for the translation candidates that are comparable across different translation resources.


Such data are available in both speech recognition and machine translation.

in the case of CLIR, the goal of query translation is not strictly equivalent to machine translation.


Multiple translation resources are believed to contribute in improving the quality of query translation.

in most previous studies, only linear combination has been used.


This means that a word index alone can never distinguish the semantic difference between these sentences.

syntactic parsers can produce different dependency relations for each sentence.


Though unreasonable, TIA did not cause very bad performance.

relaxing the assumption by adding term dependencies into the retrieval model is still a basic IR problem.


For well-defined IRs such as relational database retrieval (E. Levin et al., 2000), significant words (=keywords) are obvious.

determining significant words for more general IR task (T. Misu et al., 2004) (C.Hori et al., 2003 is not easy.


Generally, trigram model is used as acoustic model in order to improve the recognition accuracy.

monophone model is used in this paper, since the proposed estimation method needs recognition error (and IRDR).


Extracting con- Figure 2: Example of WFST for LU cepts from user utterances by keyword spotting or heuristic rules has also been proposed (Seneff, 1992) where utterances can be transformed into concepts without major modifications to the rules.

numerous complicated rules similarly need to be manually prepared.


The FILLER transition helps to suppress the insertion of incorrect concepts into LU results.

many output sequences are obtained for one utterance due to the FILLER transitions, because the utterance can be parsed with several paths.


Most prior acoustically based approaches to prosodic labeling have used local classifiers.

on phonological grounds, we expect that certain label sequences will be much more probable than others.


The subword-based UNK translation approach can be applied to all the UNKs indiscriminately.

if we know an UNK is a named entity, we can translate this UNK more accurately than using the subword-based approach.


The CWS of Hyperword generated many UNKs because of using a large size of lexicon.

if named entity recognition was applied upon the segmented results of the Hyperword, more UNKs were produced.


• Baseline+Subword: the results were made under the same conditions as the first except all of the UNKs were extracted, re-segmented by the subword CWS and translated by the subword translation models.

the named entity translation was not used.


Therefore, its performance tends to be superior in Table 6 if W 1 is used, especially for ECSet.

as W 1 occasionally retrieves few snippets, it is not able to provide sufficient information.


Therefore, their performance greatly depended on their ability to mine transliteration candidates from the Web.

this system might create errors if it cannot find a correct transliteration candidate from the retrieved Web pages.


Because of this, their system's coverage and W A were relatively poor than ours 8 .

our transliteration process was able to generate a set of transliteration hypotheses with excellent coverage and could thus achieve superior W A.  searched the Web using given source words and mined the retrieved Web pages to find target-language transliteration candidates.


In commercial translation environments, it is sometimes the case that texts are first translated by inexperienced translators and then edited by experienced translators.

this does not apply to voluntary translation.


Translations that are very literal, either lexically or structurally, are often also awkward.

a high degree of word order correspondence can be a positive sign (cf.


For example, when applying rules to 電子工業 /electronic industry, _ 工 業 ,industry is preferred than _業,industry.

in the evaluation step, precision rate of _業,industry will be reduced when applying to full morphemes, such as 電子工業 /electronic industry, and then could be filtered out if the precision is lower than the threshold.


This overloads text messages with various tasks: negotiation issues themselves, introductions and closures traditional in negotiations, and even socializing.

electronic means make the contacts less formal, allowing people to communicate more freely.


and included another entity that they termed as NUMBER.

for specific domains like weather forecasts, medical reports or student reports, more varied domain entities form slots in templates, as we observe in our data; hence, existence of a module handling domain specific entities become essential for such a task.


Instead of considering matching role sequences for a set of propositions, we could as well have considered matching bag of roles.

for the present corpus, we decided to use strict role sequence instead because of the sentences' rigid structure and absence of any passive sentences.


We decided at first to look at syntactic parse tree similarities between constituents.

there is a need to decide at what level of abstraction should one consider matching the parse trees.


As we see, the logistic regression discriminates between the majority of the examples by assigning extreme probabilities (0 and 1).

there are some examples which are extremely borderline, and thus it does not generalize well on the test set.


It shows that the extended dictionaries can translate part of Korean NEs into Chinese.

there are still many NEs that the extended dictionaries cannot cover.


Sentiment words are a basic resource for sentiment analysis and thus believed to have a great potential for applications.

it is still an open problem how we can effectively use sentiment words to improve performance of sentiment classification of sentences or documents.


Although these pieces of work aim to predict not sentence-level but document-level sentiments, their concepts are similar to ours.

all the above methods require annotated corpora for all levels, such as both subjectivity for sentences and sentiments for documents, which are fairly expensive to obtain.


One of the simplest ways to classify sentences using word-level polarities would be a majority voting, where the occurrences of positive words and those of negative words in the given sentence are counted and compared with each other.

this majority voting method has several weaknesses.


To circumvent this problem, Kennedy and Inkpen (2006) and Hu and Liu (2004) proposed to use a manually-constructed list of polarity-shifters.

it has limitations due to the diversity of expressions.


However, the bigram model cannot generalize the learned knowledge to other features such as "not great" or "not disappoint".

our polarity-shifter model learns that the word "not" causes polarity-shifts.


When we have a large amount of training data, the ngram classifier can learn well whether each n-gram tends to appear in the positive class or the negative class.

when we have only a small amount of training data, the n-gram classifier cannot capture such tendency.


As can be seen in Figure 1, the classifier managed to map the reviews onto the coordinate system.

there are very few points in the neutral region, that is, on the same X = 0 line as balanced but with low sentiment density.


Every new iteration produced an even poorer result as each new extracted corpus was of lower accuracy.

it seems that a seed list consisting of several low-frequency one-character words can compensate each other and produce better results by capturing a larger part of the corpus (thus increasing recall).


Phrase-based SMT(Statistical Machine Translation) models have advanced the state of the art in machine translation by expanding the basic unit of translation from words to phrases, which allows the local reordering of words and translation of multi-word expressions (Chiang, 2007) (Koehn et al., 2003) (Och andNey, 2004).

phrase-based SMT techniques suffer from data sparseness problems, that is; unreliable translation probabilities of low frequency phrases and low coverage in that many phrases encountered at run-time are not observed in the training data.


Moreover, we can smooth the phrase translation probability using the class of paraphrases.

eBMT or PBMT systems might translate a given sentence fast and robustly geared by sentence translation patterns or generalized transfer rules.


This process is based on sequentially classifying segments of several adjacent words.

this technique requires that entire segments have the same class label, while our technique does not.


Moreover, compared to a large-scale dictionary, our domain knowledge is much easier to obtain.

all the above models treat NER as classification or sequence labeling problem.


Since PMI ranges [-∞, +∞], the point of dividing pmi(i,p) by max pmi in Espresso is to normalize the reliability to [0, 1].

using PMI directly to estimate the reliability of a pattern when calculating the reliability of an instance may lead to unexpected results because the absolute value of PMI is highly variable across instances and patterns.


Both Basilisk and Espresso identify location names as context patterns (e.g., #東京 "Tokyo", #九州 "Kyushu"), which may be too generic to be characteristic of the domain.

tchai finds context patterns that are highly characteristic, including terms related to transportation (#+格安航空券 "discount plane ticket", #マイレージ "mileage") and accommodation (#+ホテル "hotel").


The filtering of generic patterns (green) does not show 4 Note that Basilisk and Espresso use context patterns only for the sake of collecting instances, and are not interested in the patterns per se.

they can be quite useful in characterizing the semantic categories they are acquired for, so we chose to compare them here.


Supervised learning models set their parameters using given labeled training data, and generally outperform unsupervised learning methods when trained on equal amount of training data.

creating a large labeled training corpus is very expensive and time-consuming in some real-world cases such as word sense disambiguation (WSD).


Interestingly, deciding when to stop active learning is an issue seldom mentioned issue in these studies.

it is an important practical topic, since it obviously makes no sense to continue the active learning procedure until the whole corpus has been labeled.


Thirdly, the active learning process can stop if the targeted performance level is achieved.

it is difficult to predefine an appropriate and achievable performance, since it should depend on the problem at hand and the users' requirements.


2 we can see that it is not easy to automatically determine the point of no significant performance improvement on the validation set, because points such as "20" or "80" would mislead final judgment.

we do believe that the change of performance is a good signal to stop active learning process.


The feature fusion was shown to be effective.

their methods hinge on the availability of a labeled bilingual corpus.


(2006)), an ISO standard for lexical resource modelling, and an LMF version of FrameNet exists.

we believe that our usage of a typed formalism takes advantage of a strong logical foundation and the notions of inheritance and entailment (cf.


Finally, the closest neighbour to our proposal is the ATLAS project (Laprun et al., 2002), which combines annotations with a descriptive meta-model.

to our knowledge, ATLAS only models basic consistency constraints, and does not capture dependencies between different layers of annotation.


In the case of semantic roles, for example, such a regularity would be that semantic roles are not assigned to conflicting grammatical functions (e.g., deep subject and object) within a given lemma.

some of the role sets we extracted contained exactly such configurations.


For example, " (wei)" is a classifier used when counting people with honorific perspective.

judgement if " " can modify nouns such as "political prisoner" or "local villain" is rather uncertain.


Note that such relations are discarded as redundant ones.

the "indirect" case includes the relations which can not be extracted from the database but only inferred by using transitivity on the taxonomy.


In the objective evaluation, this answer is marked as incorrect.

some users may find this snippet useful, although they may still prefer the seventh or eighth text snippets from Table 1 as primary answers, as they mention Barbara Jordan's election to a state legislature in 1966, and to the Congress in 1972.


Recent systems at NTCIR-6 1 Question Answering Challenge (QAC-4) can handle why-questions .

their performance is much lower  than that of factoid QA systems (Fukumoto et al., 2004;Voorhees and Dang, 2005).


Such patterns include typical cue phrases and POS-tag sequences related to causality, such as "because of" and "by reason of."

as noted in (Inui and Okumura, 2005), causes are expressed in various forms, and it is difficult to cover all such expressions by hand.


In the CoNLL-2005 shared task (SRL for English), the best system found causal adjuncts with a reasonable accuracy of 65% (Màrquez et al., 2005).

when we analyzed the data, we found that more than half of the causal adjuncts contain explicit cues such as "because."


A:000217262,L3 A mother panda often gives birth to two cubs, but when there are two cubs, one is discarded, and young mothers sometimes crush their babies to death.

a:000406060,L6 because of the recent development in the midland, they are becoming extinct.


Traditional QA query expansion is based only on the individual keywords in a question.

the cluster-based expansion learns features from context shared by similar training questions from a cluster.


In the above {the U.N. body, Kosovo} example, it may be difficult to determine the relationship between U.N. body and Kosovo straightforwardly.

making the right guess about the relationship is easier if the U.N. body is grouped with army and government.


(Huang et al., 2004) exploited the Web as a training corpus to train a classifier with user-defined categories.

it is widely recognized that when using documents on the Web users must spend a great deal of time filtering out unrelated contents.


Computer users increasingly need to produce text written in multiple languages.

typical computer interfaces require the user to change the text entry software each time a different language is used.


In our study, IMEs for European languages are built using simple transliteration: e.g., "[" typed in an English keyboard is transliterated into "ü" of German.

the IMEs for Japanese and Chinese Figure 3: Entry Flow require a more complicated system because in these languages there are several candidate transcriptions for a key sequence.


This outperformed disambiguation using VSM, demonstrating the utility of the taxonomic information in Wikipedia and WordNet.

because not all words in Wikipedia have categories, and there are very few named entities in WordNet, the number of disambiguated words that can be obtained with MCAT (2,239) is less than when using VSM, (12,906).


Similar rule-based systems exist for Spanish and Italian (Saquete et al., 2006).

such resources are restricted to a handful of languages.


The model which combines the optimal settings for timexes and events outperforms the uninformed baseline by 17.93% (timexes) and 6.64% (events) F 1 -measure.

exploration of the model space on the basis of the (larger and thus presumably more representative) test set shows that the optimised models do not generalise well.


(2006) obtain, for example, an entailment relation X wins → X plays from such a pattern as player wins.

their way of using nominalized verbs is highly limited compared with our way of using verbal nouns.


Because of its good tradeoff between efficiency and expressiveness, BTG restriction is widely used for reordering in SMT (Zens et al., 2004).

bTG restriction does not provide a mechanism to predict final orders between two neighboring blocks.


(2005) adapted the hook trick which changes the time complexity from O(n 3+4(m−1) ) to O(n 3+3(m−1) ).

the implementation of the hook trick with pruning is quite complicated.


The major goal of personalized search is to accurately model a user's information need and store it in the user profile and then re-rank the results to suit to the user's interests using the user profile.

understanding a user's information need is, unfortunately, a very difficult task partly because it is difficult to model the search process which is a cognitive process and partly because it is difficult to characterize a user and his preferences and goals.


We use relevance feedback for personalization in our approach.

we propose a novel usage of relevance feedback to effectively model the process of query formulation and better characterize how a user relates his query to the document that he intends to retrieve as discussed in the web search process above.


Most common errors among the proposed methods were generated by a transformation pattern Typically, dropping a nominal element N 1 of the given nominal compound N 1 : N 2 generalizes the meaning that the compound conveys, and thus results correct paraphrases.

it caused errors in some cases; for example, since N 1 was the semantic head in (7), dropping it was incorrect.


Consequently, for each stored word, we find its stem.


Therefore, Japanese NER has tight relation with morphological analysis, and thus it is often performed immediately after morphological analysis Yamada, 2007).

such approaches rely only on local context.


For example, while "Kawasaki" in the second sentence of (2) is the name of a person, "Kawasaki" in the second sentence of (3) is the name of a soccer team.

the second sentences of (2) and (3) are exactly the same, and thus it is impossible to correctly distinguish these NE classes by only using information obtained from the second sentences.


This clue is considered by using cache features to a certain extent.

if the same morpheme is not used, cache features cannot work.


The system then issues a new S-Query ['I have' AND 'this person for years'], and finally mines the new set of snippets to discover that 'known' is the preferred lexical option.

to proofing determiner usages errors, mf req: mf req = f requency of matched collocational verb/adj.


Thus, a false advertisement may be proposed.

the system may have enough information at the later stages.


On the other hand, the system may have enough information at the later stages.

users may complete their talk at any time in this case, so the advertisement effect may be lowered.


In this paper, only one intention is assigned to the utterances.

there may be many participants involving in a conversation, and the topics they are talking about in a dialogue may be more than one.


This result can be ranked by using criteria such as PageRank (Brin and Page, 1998) or relevancy to the query.

this long flat list is uncomfortable to users, since it forces users to examine each page one by one, and to spend significant time and effort for finding the really relevant information.


In our test result, when using Dirichlet smoothing, the precision of top 5 and 10 labels are 82% and 80%, thus users can benefit in browsing from our model using 5 or 10 labels.

the precision rapidly drops to 60% at P @20.


We used Google's search results as an input to our system.

multiple engines offer a better coverage of the web because of the low overlap of current search engines (Bharat and Broder, 1998).


In this method, impression keywords that are related to the source word are used.

a user must provide impression keywords, which is time-consuming and expensive.


A preliminary study showed that the language model adaptation was generally effective for transliteration.

because the focus of this paper is the related term extraction, we do not describe the evaluation of the language model adaptation.


The last three lines are the results of Pharaoh with phrase length from 1 to 3.

the length of 2 http://www.statmt.org/wmt06/shared-task/ 3 http://www.cs.utah.edu/~hal/searn/SimpleSearn.tgz  phrases for Amasis is determined by ITG-like tree nodes and there is no restriction for it.


There are significantly more single word translations in our method.

the translation quality can be kept at the same level under this circumstance.


A parallel corpus that has similar statistical characteristics to the target domain should yield a more efficient translation model.

domainmismatched training data might reduce the translation model's performance.


Target language model probability (weight = 0.5) According to a previous study, the minimum error rate training (MERT) (Och, 2003), which is the optimization of feature weights by maximizing the BLEU score on the development set, can improve the performance of a system.

the range of improvement is not stable because the MERT algorithm uses random numbers while searching for the optimum weights.


The experimental results show that target language side information gives the best performance in the experimental setting.

there are no large differences among the different selection results.


Often more data is better data, and so it should come as no surprise that recently statistical machine translation (SMT) systems have been improved by the use of large language models (LM).

training data for LMs often comes from diverse sources, some of them are quite different from the target domain of the MT application.


SMT decoders such as Moses  may store the translation model in an efficient on-disk data structure (Zens and Ney, 2007), leaving almost the entire working memory for LM storage.

this means for 32-bit machines a limit of 3 GB for the LM.


Since the order of English words is fixed, the number of different n-grams that need to be looked up is dramatically reduced.

since the n-best list is only the tip of the iceberg of possible translations, we may miss the translation that we would have found with a LM integrated into the decoding process.


The highest score, CS=4, is assigned to the synset that is evident to include more than one English equivalent of the lexical entry in question.

the lowest score, CS=1, is assigned to any synset that occupies only one of the English equivalents of the lexical entry in question when multiple English equivalents exist.


The number of languages that have been successfully developed their Word-Nets is still limited to some active research in this area.

the extensive development of WordNet in other languages is important, not only to help in implementing NLP applications in each language, but also in inter-linking WordNets of different languages to develop multi-lingual applications to overcome the language barrier.


On the other hand, for example, " (matter)" is usually written in hiragana (" ") in word definition.

it is difficult to know automatically that a word " " in word definition means " ", since the dictionary has other entries which has the same reading "koto", such as " (Japanese harp)" and " (ancient city)".


From the result, we can find that not only common words which may be included in a "basic vocabulary", such as " (exist)", " (certain/some)" 2 , " (do')", " (thing)", etc., but also words which are not so common but are often used in 2 It is used to say something undetermined or to avoid saying something exactly even if you know that.

some words in the top ranked words, such as "A" and "B", seem not to be appropriate for defining vocabulary.


In case a system paraphrases a functional expression f into f , it also should generate all variants of f in potential.

any proposed system does not guarantee this requirement.


Loosening the constraints allows more sentences to be parsed, thus increasing the coverage, but at the same time easily leads into overgeneration, problems with disambiguation and decreased preciseness.

the points that we raised above indicate that there is a strong relationship between coverage and preciseness.


Consequently, we argue that coverage can be used a measure of generalizability; It sets the upper bound for the performance on the sentence-level evaluation measures.

the evaluation should always be accompanied with data on the preciseness of the parser and the level of detail in its output.


StatCCG (Preliminary public release, 14 January 2004) is a statistical parser for CCG that was developed by Julia Hockenmaier (2003).

to C&C, this parser is based on a generative probabilistic model.


The key factor in our success was the extraction of only reliable information from unlabeled data.


To use this method more effectively, we need to balance the labeled and unlabeled data very carefully.

this method is not scalable because the training time increases significantly as the size of a training set expands.


These presented methods used similarity measures heuristically according to the property of the languages.

detecting conjunctive boundaries with a similar method in Chinese may meet some problems, since a Chinese word may play different syntactic functions without inflection.


Generally in the process of multi-document text summarization, a collection of input documents about a particular subject is received from the user and a coherent summary without redundant information is generated.

several challenges exist in this process the most important of which are removing redundant information from the input sentences and ordering them properly in the output summary.


The sectional evaluation and the inspection of example output show that this system works well.

larger scale evaluation and comparison of its accuracy remain to be future work.


In general, if an ordering gets a positive τ value, the ordering can be considered to be better than a random one.

for a negative τ value, the ordering can be considered to be worse than a random one.


This can be seen as an advantage of Ca over Fa.

we can see that the adjacency window size still influenced the performance as it did for Fa.


PrepNet is structured in two levels: • the abstract notion level: global, language independent, characterization of preposition senses in an abstract way, where frames represent some generic semantic aspects of these notions, • the language realization levels that deal with realizations for various languages, using a variety of marks (postpositions, affixes, compounds, etc.).

we will keep the term 'preposition' hereafter for all these marks.


So far, the formalism we have elaborated allows us to encode syntactic frames, restrictions, case marks, prefixes and suffixes as well as postpositions.

languages of the malayo-polynesian family raise additional problems which are not so easy to capture.


Those roles are related to a variety of situations which are not necessarily introduced by prepositions.

a preliminary, exploratory, task could be to attempt to classify FrameNet roles under the main abstract notions of PrepNet.


It clearly demonstrates that our FGTKs are faster than the GTK algorithm as expected.

the improvement seems not so significant.


So far, the effectiveness of handling expressive divergence has been shown for IR using a thesaurus-based query expansion (Voorhees, 1994;Jacquemin et al., 1997).

their methods are based on a bag-of-words approach and thus does not pay attention to sentence-level synonymy with syntactic structure.


4 This is a starting point for having t-nodes corresponding to lexias.

in the current state it is not fully sufficient even for verbs, mainly because parts of MWEs are not joined into one node.


Obviously, the posterior probability p(W |X) is a good confidence measure for the recognition decision that X is recognized as W .

most realworld ASR systems simply ignore the term p(X) during the search, since it is constant across different words W .


The main problem when applying ASR to extremely inflected languages such as Telugu, is the need to use a very large vocabulary, in order to reduce the OOV rate to an acceptable level.

this causes problems for making the automatic transcription in a time close to the real-time.


In our proposed method, w is estimated to maximizeF μ (Θ, w).

training dataset D is also used to estimate Θ.


MC-F μ , MC-F M , and MC-F L outperformed MML as regards the three F 1 -scores for JPAT.

mmL performed better for Reuters than mC-F μ , mC-F m , and mC-F L , and provided a better F Lscore for WIPO.


Although the feature spaces of the dependency path kernels are not subsets of the subsequence kernel, we can clearly see that we get higher precisions by introducing bias towards the syntactically more meaningful feature space.

the dependency path kernel is fairly rigid and imposes many hard constraints such as requiring the two paths to have exactly the same number of nodes.


McCallum(McCallum et al., 2000) improved them by allowing a record to locate in plural blocks in order to avoid detection loss.

the problems of these filtering methods using blocking are that the user needs trial and error parameters such as first n terms for Standard Blocking, and that these incur detection loss in spite of improvements being attempted, caused by two documents of a correct document pair existing in different blocks.


the CMU UIMA component repository 1 , GATE (Cunningham et al., 2002) with its UIMA interoperability layer, etc.

simply wrapping existing modules to be UIMA compliant does not offer a complete solution.


For a given name p, they search for the query * koto p 1 and extract the words that match the asterisk.

koto is highly ambiguous and extracts lots of incorrect aliases.


As we iteratively added new labeled sentences into the training set, the precision scores of active learning were steadily better than that of passive learning as the uncertain examples were added to strengthen existing labels.


That is only true for modularitybased greedy algorithms that select vertices pairings be merged into a cluster at each step of the tree-form integration process based on modularity optimization criterion.

such methods suffer from the problem that once a merger is executed based on a discrimination error, there is no chance of subsequently splitting pairings that belong to different subgroups.


It is supposed that if the MCL is applied to word association or co-occurrence data it will yield concept clusters where words are classified according to similar topics or similar meanings as paradigms.

because the word distribution of a corpus approximately follows Zipf's law and produces a small-world scale-free network (Steyvers et al., 2005), the MCL will result in a biased distribution of cluster sizes, with a few extraordinarily large core clusters that lack any particular features.


The connection rate in the core cluster is very low (0.002 with and 0 without the hub), as is the modularity Q value for the MCL (0.094).

subdivision of the core cluster in the BMCL results yielded a high modularity Q value (0.606) when latent adjacencies derived from bypassing connections with a threshold of q θ =3 were used.


The standard Belief Network actually supposes that all the relationships are and.


We can notice that we will stay in state zero as long as the output is identical to the input.

we can move from state zero, which corresponds to edit distance zero, to state one, which corresponds to edit distance one, with three different ways: • input is mapped to a different output (input is consumed and a different symbol is emitted) which corresponds to a substitution, • input is mapped to an epsilon (input is consumed and no output emitted) which corresponds to a deletion, and • an epsilon is mapped to an output (output is emitted without consuming any input) which corresponds to an insertion.


It should be mention that the result of SVM fluctuates slightly, which is due to different number of testing examples.

tSVM and tSVM using argument-specific heuristics improve highly as the increase in untagged data size.


ProMED-mail is an Internet-based system that provides reports by public health experts concerning outbreak diseases (that is, the system is not automatic but rather human curated).

to ProMED-mail, MedI-Sys is an automatic system working on multilingual languages, but it mainly focuses on analyzing news stories based on the country level.


For example, if there is a news story about equine influenza in Camden, the system should detect that the disease name is "equine influenza" and the location name is "Camden".

there are two locations named Camden: One in Australia and one in London, UK.


Sighted computer users spend a lot of time reading items on-screen to do their regular tasks such as checking email, fill out spreadsheets, gather information from internet, prepare and edit documents, and much more.

visually impaired people cannot perform these tasks without an assistance from other, or without using assistive technologies.


Early studies on PPI extraction employ feature-based methods.

the featurebased methods often fail to effectively capture the structured information, which is essential to identify the relationship between two proteins in a constituent or dependency-based syntactic representation.


These approaches have been proved to be both algorithmically appealing and empirically successful.

most of current syntax-based SMT systems use IBM models (Brown et al., 1993) and hidden Markov model (HMM) (Vogel et al., 1996) to generate word alignments.


Through referring to the word alignment shown in Figure 1, we can collect the target spans which are {5}, {4,0,1,2,3,6,15}, and {7,8,9,10,11,12,13} respectively for t0, c3, and c16.

we cannot sort these three spans since there are overlapping between the first two spans 3 .


The problem of permuting the source string to unfold the crossing alignments is computationally intractable (see (Tromble and Eisner, 2009)).

various constraints can be made on unfolding the crossing alignments in a.


Source reordering for PBSMT assumes that permuting the source words to minimize the order differences with the target sentence could improve translation performance.


The basic word based lexicalized reordering model uses neighboring words to perform the orientation estimates.

since words are not always translated by themselves, these estimates can be improved by considering neighboring phrases rather than words.


In most applications including temporal relation classification, the preparation of such samples is a hard, time consuming, and expensive task (Mani et al., 2006).

all these annotated samples may not be useful, because some samples contain little (or even no) new information.


The official best result in the closed test achieved an F score of 95.00, and our result is quite close to that, ranked 4th of 23 official runs.

our method took about 30% less than the OWL-QN method in the training time.


This will lead to poor performance on unseen data.

if these words are correctly attributed to the subject topic, then the high ratings will appropriately be attributed to the unconditional positive words appearing in the reviews.


According to (Nicolae & Nicolae, 2006), bestcut does not utilize coreferent pairs involving pronouns.

event coreference chains contain a significant proportion of pronouns (18.8% of event coreference mentions in the On-toNotes2.0 corpus).


Then it evaluates the semanticmatching features to pair-up mentions from the same semantic type.

event NPs exhibit a very different hierarchy in WordNet from the object NPs.


We showed an improvement of up to 1.5 bleu points on the in domain data set and an improvement of 0.4 bleu on the generic test set.

there are still some errors in respect to the morphology of the verb phrases, which the Language Model is unable to tackle.


For example, given a word " ", these methods try to select the most appropriate pronunciation out of the three dictionary entries: ninki (popularity), hitoke (sign of life) and jinki (people's atmosphere), depending on the context.

in these approaches, seg-mentation errors tend to result in the failure of the following step of pronunciation prediction.


After inspecting the errors manually, we have found that this is because the UniDicbased operations do not include many single-kanji pronunciations that are commonly used in person's names, such as " mi" and " to".

this problems seems negligible when a larger dictionary including common pronunciations for person's names is available.


In the 20-best output, we find the correct solution for many words with ambiguous characters as shown in Table 13.

if a word contains two ambigu- Table 12: Pronunciation differences between Hindi and Urdu ous characters, it was difficult for the transliterator to transliterate it correctly.


Recently, many researches have been devoted to NE transliteration (most person names) or NE meaning translation (organization names) individually.

there are still two main challenges in statistical Chinese-English (C2E) NE translation.


So we use NE alignment result to evaluate the phonetic similarity of two segments by 1 1 where i e denotes the same syllables they aligned in the training set.

a global concept, which is borrowed from tf×idf scheme in information retrieval (Chen et al., 2003), is used in Eq (7).


2004;Oh and Choi, 2005;Pervouchine et al., 2009;Durrani et al., 2010).

for NE meaning translation, (Zhang et al., 2005;Chen and Zong, 2008; have proposed different statistical translation models only for organization names.


So far, semantic transliteration has been proposed for learning language origin and gender information of person names (Li et al., 2007).

semantic information is various for NE translation.


Recently, natural language processing research has begun to pay attention to second language learning (Rozovskaya and Roth, 2011;Park and Levy, 2011;Liu et al., 2011;Oyama and Matsumoto, 2010;Xue and Hwa, 2010).

most previous research for second language learning deals with restricted types of learners' errors.


For example, research for JSL learners' errors mainly focus on Japanese case particles (Oyama and Matsumoto, 2010;Imaeda et al., 2003;Nampo et al., 2007;Suzuki and Toutanova, 2006), however they focus only on case particles whereas we attempt to correct all types of errors.

real JSL learners' writing contains not only errors of Japanese case particles but also various other errors including spelling and collocation errors.


Users can submit a free composition on a subject and receive feedbacks from other users of the native language.

they are not able to write about arbitrary topics.


It is annotated with error types with correct forms to allow error analysis.

similar to Teramura Error Data, the corpus does not cover many topics because it was collected at only four institutions.


When translating a sentence from Japanese to another language with SMT, one usually performs word segmentation as a pre-processing step.

jSL learners' sentences contain a lot of errors and hiragana (phonetic characters), which are hard to tokenize by traditional morphological analyzer trained on newswire text.


Precision and Recall in this setting were reported at 87.9% and 28.2%, respectively.

we should also note that in these experiments text was also fragmented into snippets, and similar to what Luyckx and Daelemans did, the similarity model uses fragments of the same source text to predict authorship.


Frequencies of character n-grams have also been successfully used to build author profiles (Keselj et al., 2003).

to the best of our knowledge, this is the first work exploiting characterbased language models for AA, although, Raghavan et al.


This is not surprising since the meta features aggregate posts from different authors depending on similarity and thus predicting authorship only on these features does not work as well as the standard approach of using first level features.

these meta features do outperform the bag of words baseline results (compare column MSMF in Table 6 with results shown in Table 4), underscoring the fact that the CHE data set is harder than the typical text classification task where the lexical features by themselves can solve the problem with very high accuracy.


The experimental evaluation presented here shows that a relatively inexpensive approach based on PCFGs can scale up to a larger number of authors, even if the documents are only a couple of sentences long.

this syntactically driven approach is outperformed by our proposed modality specific meta features framework.


Gerber and Chai (2010) proposed analysis of English nominal predicates with this similarity to take discourse context into account.

the similarity measure they used has drawbacks: it requires a co-reference resolver and a large number of documents.


Though the topic changes between two sentences, A and B cannot take it into account this and output " (Marriages)" which is an argument of " (increase)" because the frequencybased feature is active.

c and D handle this because the similarity between the nominative case of " " and " " is low.


There is not much evidence to skew the distribution from uniform for rare words.

when more evidence is available, the distribution becomes smoothly skewed to reflect the different syntactic preferences of the individual words, and it can eventually become as spiky as in the other approaches given sufficient evidence.


Intuitively, creating more subcategories can increase parsing accuracy.

oversplitting can be a serious problem, the details were presented in Klein and Manning (2003).


As with their work, we also use semantic knowledge for parsing.

our gold is to employ HowNet hierarchical semantic knowledge to generate fine-grained features to dependency parsing, rather than to PCFGs, requiring a substantially different model formulation.


Blazing depends on the fact that the ERG and the GTB both have a theoretical linguistic underpinning, and so we expect they would share many assumptions about phrase structure, particularly for phenomena such as PP-attachment and co-ordination.

there are also disparities, even between the unlabelled bracketing of the GTB and ERG trees.


This is partially to be expected, since some of the treebanking will be taken up with unavoidable tasks such as evaluating whether the final tree is acceptable that blazing cannot avoid.

the 8% reduction in mean annotation time for annotator 2 is still fairly modest.


6 Additional Experiments We believe that the entities in a document that cooccur with a target entity are important clues for disambiguating entities.

while we had ready access to named entity recognizers in English, we did not have NER capability in all of the languages of our collection.


(2008) used seed sets of entities and search engines to collect NER training data from the web.

constructing of a high-quality seed list is also a time-consuming work.


From Figure 4(a), it is seen that the perplexity values decreases dramatically when the iteration times are below 200. number of topics starts to increase.

after a certain point, the perplexity values start to increase.


TRLM learns the word-to-word translation probabilities from parallel corpus collected from question answer archives.

tMC models wordcategory-topic distribution from the whole question answer content.


Previous studies (Krogel and Scheffer, 2004) show that if the independence assumption of co-training approach is violated, the co-training approach can yield negative results.

our results show that co-training yields improvement even though are classifiers are not independent.


In this paper, we aim at detecting event-relevant sentences in a news corpus given an event query.

to previous work that needs expensive annotation for query-answer pairs, we use unlabeled news summaries that are readily available in large quantities online and design an efficient semi-supervised learning strategy for event identification based on co-training with Bayesian network classifiers.


Recently, corpus-based approaches have been successfully adopted in the field of Japanese Linguistics.

the central part of the fields has been occupied by historical research that uses ancient material, on which fundamental annotations are often not yet available.


They are spelled with Kana and a voiced consonant mark ( ) to the upper right (see Figure 1).

confusingly, it was not ungrammatical to put down the character without the mark to represent voiced syllable 2 In historical linguistics, the phrase "modern Japanese" refers to the language from 1600 on to the present in a broad sense.


In the modern Japanese literary text, orthographic variations are not only the unmarked.

unmarked characters appear a lot in the text and can be annotated easily by hand.


This is another piece of evidence that discriminative language model works quit well for this task.

both Character type n-gram and Markedness n-gram contribute to improvement of precision.


(2007) proposed a character-based joint method for word segmentation and POS tagging, in which they introduced an unsupervised method for unknown word learning.

they only learned the unknown words from the test set.


Hence we note the loss of dual inflection marking and nominative case marking.

clitization is more complex in DA as follows: EGY mAHkytl-hAlhw$, , 'she did not recount it to him' is expressed in three words in MSA as lm tHkyhA lh .


In the next example, BEDM gives a lower recall than LEDM.

the combined metric does better than the individual edit distance metrics in this example too.


For example, for a sentence commenting on a digital camera like "40D handles noise very well", terms such as "noise" and "well" are significant feature terms for the node "noise".

the term "noise" becomes insignificant for its child nodes "noise +" and "noise -", since the hierarchical classification characteristic of the HL-SOT approach that a node only processes target texts which are labeled as true by its parent node ensures that each target text handled by the nodes "noise +" and "noise -" is already classified as related to "noise".


In a most recent research work (Wei and Gulla, 2010), Wei and Gulla proposed the HL-SOT approach that sufficiently utilizes the hierarchical relationships among a product attributes and solves the sentiment analysis problem in a hierarchical classification process.

the HL-SOT approach proposed in (Wei and Gulla, 2010) uses a globally unified index term space to encode target texts for different nodes which is deemed to limit the performance of the HL-SOT approach.


Evaluating the correctness of discourse parsing is a hard task.

it is not of prime importance for our task that the segments are correct according to any discourse theory but that they do not include passages containing differing labels according to the gold standard.


We believe that it is because the opinion annotation on dependency relations is more difficult than on words, sentences, or documents.

because of this alignment, we are able to see the distribution of different dependency relations in opinion sentences and opinion segments (opinion trios).


NTCIR annotated opinions, polarities, sources, and targets for its multilingual opinion analysis task (MOAT, Seki et al., 2008).

none of them were annotated on materials with syntac-tic structures, and it caused the lack of analysis of opinion syntactic structures.


The polarity of an adjectival modifier is often propagated to the polarity of the modified noun or noun phrases with no inherent polarity.

sometimes the polarity is not propagated to that of the enclosing clause or sentence at all.


Our work is similar to their work in that we followed the idea of the Principle of Compositionality.

our focus is on examining the characteristics of context surrounding a given adjectival modifier when its polarity is either propagated or not propagated and seeing how this propagation result affects the overall polarity of the clause.


We believe that this is because if the polarity of the top node word is explicitly 'positive' because of its inherent polarity the overall polarity of the clause is obviously 'positive' regardless of the result of the polarity propagation decision.

in the case of 'neutral' clause, the correct polarity propagation decision for 'UNSHIFT' is critical for detecting the overall polarity.


The detection results of the overall sentiment at the clause level are meaningfully enhanced as compared to those based on the previous polarity propagation rules regarding especially 'neutral' sentences.

despite the correct decision for 'UNSHIFT', we found that such polarity of the modifiers may also help to identify the implicit sentiment without further deeper linguistic analysis.


Therefore, when a user issues a query, recommending tweets of good quality has become extremely important to satisfy the user's information need: how can we retrieve trustworthy and informative posts to users?

we must note that Twitter is a social networking service that encourages various content such as news reports, personal updates, babbles, conversations, etc.


However, because of the diversity of the extracted relations and the domain independence, open relation extraction is probably not suitable for populating relational databases or knowledgebases.

the task of extracting relation descriptors as we have proposed still assumes a pre-defined general relation type, which ensures that the extracted tuples follow the same relation definition and thus can be used in applications such as populating relational databases.


In the early days of the speller, the dictionary was manually compiled by lexicographers.

it is time consuming to construct a broad coverage dictionary, and domain knowledge is required to achieve high quality.


Their reranking method had the advantageous ability to incorporate clickthrough logs to a translation model learned as a ranking-feature.

their methods are based on edit distance, and thus they did not deal with the task of synonym replacement and acronym expansion.


We consider that this issue can be solved at some level by generating a language model using the first term only, after splitting queries separated by a space in search query logs.

attribute words are not always separated by a space, and sometimes appear as the first term in the query 5 .


A keyword may exist in multiple articles.

several keywords cam uniquely identify a document if they are grouped together as a keyword set (Jiang et al., 2009).


(2010) proposed the use of crosslinguistic knowledge represented as a set of allowable head-dependent pairs.

this method still requires provision of language-specific rules to boost accuracy.


Each constituent type is denoted by C : w, where C is a syntactic category and w is the head word, such as s\ > np/ < np : eats.

a simple parametric syntactic prototype will give rise to parsing failures when faced with parametrically exceptional items, which occur in most if not all languages.


Each clue has been empirically proven to be effective for coordination disambiguation.

a unified approach that combines both clues has not been explored comprehensively.


Recent research efforts have led to the development of a state-of-the-art supervised coreference model that can address all of the aforementioned problems, namely the joint cluster-ranking (CR) model (Rahman and Ng, 2009).

other than its superior empirical performance to competing coreference models (such as the MP model), little is known about the joint CR model.


If NP k is anaphoric, the rank of i(c j , NP k ) is HIGH if NP k belongs to c j , and LOW otherwise.

if NP k is non-anaphoric, the rank of i(c j , NP k ) is LOW unless c j corresponds to the NULL cluster, in which case its rank is HIGH.


Hence, if we were to train an SVM classifier, all we need to do is to design a kernel.

we are given a ranking problem, and it is not immediately clear how an SVM can learn a ranking model in the presence of tree-based features.


If both feature vectors contain only flat features, the subtraction is straightforward, since each flat feature is real-valued.

if one of the feature vectors has a tree-based feature 3 (which happens when c i or c j is NULL), we handle the flat features and the tree-based feature separately.


If both instances contain only flat features, we simply employ a normalized linear kernel, which computes similarity as the cosine of their feature vectors.

if one or both of them has a tree-based feature, a linear ker-nel is not directly applicable.


Recently, Woodsend and Lapata (2011) proposed a framework to combine treebased simplification with ILP.

to sentence compression, sentence simplification generates multiple sentences from one input sentence and tries to preserve the meaning of the original sentence.


Our first attempt is to collect data automatically from original English and Simple English Wikipedia, based on the suggestions of Napoles and Dredze (2010).

we found that the collected corpus is unsuitable for our model.


The expected outcome is when we use a larger stack-size the decoder may has more chance to find better hypotheses.

a larger stack-size will obviously cost more memory and run time is slower.


The rich information offered by these systems provides additional clues for collaboratively summarizing online documents in a social context.

most existing methods generate a summary based only on the information within each document or its neighboring documents, while the social context is usually ignored.


Without doubt, effective recognition of them provides a good basis for theme-based summarization.

summaries generated in such a way are not guaran-teed to cater to the user's information need and therefore may not always be in line with his/her expectations.


Some other works (Hirao et al., 2002;Iwasaki et al., 2005;Murray et al., 2005;Shen et al., 2007;Byrd et al., 2008;Fujii et al., 2008) can generate summaries satisfying such requirements.

these methods generally require manual work of creating rules or training data.


"contact reason", "response"), where the method or lead method is used.

the method requires training data to identify the sentence type of each sentence.


The lead method extracts sentences from the top in order under the assumption that the important points are described first.

important parts such as the customer's requirements and agent's responses can be located anywhere because the customer's requirements are identified through conversation interactions, which differ according to customers.